AI-driven wear prediction for linear guides with physical constraints toward resilient manufacturing systems

Abstract

Monitoring the progressive degradation of linear guide systems under complex operating conditions is difficult using weak vibration signals, and existing data-driven methods generally suffer from insufficient integration of physical evolution laws and poor calibration of predictive uncertainty. This issue is particularly consequential in high-precision manufacturing, where unexpected wear-induced failures of linear guides can disrupt production continuity and undermine system resilience. To address these problems, this paper proposes an AI-driven wear prediction framework that integrates physical priors with adaptive physical constraints. First, coarse-grained physical prior features are extracted from wear profile data and concatenated with PCA-reduced vibration features to establish a dual-channel fusion mechanism. Second, a lightweight residual network enhances degradation feature extraction, while adaptive physical constraints dynamically balance prediction accuracy with physical regularity. Finally, within a pseudo-Bayesian physics-informed framework, a two-stage model selection and global variance scaling strategy is proposed to achieve well-calibrated uncertainty quantification under weak signal conditions. Experimental results on a linear guide degradation dataset show that the proposed method achieves an R² of 0.9553, MAE of 101.70 μm·mm, and RMSE of 143.26 μm·mm, with a 95% prediction interval coverage rate of 0.9460. These results demonstrate that the proposed framework provides reliable, uncertainty-aware wear-state prediction and RUL-oriented decision support for the predictive maintenance of linear guides. By enabling early degradation warning, informed maintenance planning, and rapid recovery preparation, the proposed method can contribute to reducing unplanned downtime and supporting the resilience of high-precision manufacturing systems.